Using satellite radiotelemetry data to delineate and manage wildlife populations
Bibliographic record
Abstract
The greatest promise of radiotelemetry always has been a better understanding of animal movements. Telemetry has helped us know when animals are active, how active they are, how far and how fast they move, the geographic areas they occupy, and whether individuals vary in these traits. Unfortunately, the inability to estimate the error in animals' utilization distributions (UDs), has prevented probabilistic linkage of movements data, which are always retrospective, with future management actions. We used the example of the harvested population of polar bears (Ursus maritimus) in the Southern Beaufort Sea to illustrate a method that provides that linkage. We employed a 2-dimensional Gaussian kernel density estimator to smooth and scale frequencies of polar bear radio locations within cells of a grid overlying our study area. True 2-dimensional smoothing allowed us to create accurate descriptions of the UDs of individuals and groups of bears. We used a new method of clustering, based upon the relative use collared bears made of each cell in our grid, to assign individual animals to populations. We applied the fast Fourier transform to make bootstrapped estimates of the error in UDs computationally feasible. Clustering and kernel smoothing identified 3 populations of polar bears in the region between Wrangel Island, Russia, and Banks Island, Canada. The relative probability of occurrence of animals from each population varied significantly among grid cells distributed across the study area. We displayed occurrence probabilities as contour maps wherein each contour line corresponded with a change in relative probability. Only at the edges of our study area and in some offshore regions were bootstrapped estimates of error in occurrence probabilities too high to allow prediction. Error estimates, which also were displayed as contours, allowed us to show that occurrence probabilities did not vary by season. Near Barrow, Alaska, 50% of bears observed are predicted to be from the Chukchi Sea population and 50% from the Southern Beaufort Sea population. At Tuktoyaktuk, Northwest Territories, Canada, 50% are from the Southern Beaufort Sea and 50% from the Northern Beaufort Sea population. The methods described here will aid managers of all wildlife that can be studied by telemetry to allocate harvests and other human perturbations to the appropriate populations, make risk assessments, and predict impacts of human activities. They will aid researchers by providing the refined descriptions of study populations that are necessary for population estimation and other investigative tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".